Coronavirus-19 and coagulopathy: A Systematic Review [COVID-COAG]
Bibliographic record
Abstract
ABSTRACT Background Understanding the association between Coronavirus Disease 2019 (COVID-19) and coagulopathy may assist clinical prognostication, and influence treatment and outcomes. We aimed to systematically describe the relationship between hemostatic laboratory parameters and important clinical outcomes among adults with COVID-19. Methods A systematic review of randomized clinical trials, observational studies and case series published in PubMed (Medline), EMBASE, and CENTRAL from December 1, 2019 to March 25, 2020. Studies of adult patients with COVID-19 that reported at least one hemostatic laboratory parameter were included. Results Data were extracted from 57 studies (N=12,050 patients) that met inclusion criteria. The average age of patients was 52 years and 45% were women. Of the included studies, 92.7% (N=38/41 studies) reported an average platelet count ≥ 150 × 10 9 /L, 68.2% (N=15/22 studies) reported an average prothrombin time (PT) between 11-14 s, 55% (N=11/20 studies) reported an average activated partial thromboplastin time (aPTT) between 25-35 s, and 34.4% (N=11/32 studies) reported a D-dimer concentration above the upper limit of normal (ULN). Eight studies (7 cohorts and 1 case series) reported hemostatic lab values for survivors versus non-survivors. Among non-survivors, D-dimer concentrations were reported in 4 studies and all reported an average above the ULN. Interpretation Most patients had a normal platelet count, elevated D-dimer, PT and aPTT values in the upper reference interval; D-dimer elevation appeared to correlate with poor outcomes. Further studies are needed to better correlate these hemostatic parameters with the risk of adverse outcomes such as thrombosis and bleeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".